Description: This paper investigates the use of models of dependent mortality for determining annuity values. We discuss a broad class of parametric models using a bivariate survivorship function called a copula. Using data from a large insurance company to illustrate our methods, we calculate maximum likelihood estimates to calibrate the rnodel.

Description: This presentation develops a micro–level stochastic model for the run–off of automobile insurance claims. The analysis uses policy exposure, claims and payments experience data from vehicle insurance portfolios obtained from a major general insurance company in Singapore. The data are from the General Insurance Association 'GIA' of Singapore, an organization comprising most of the general insurers in Singapore. Modelling the run–off of individual Reported But Not Settled [RBNS] claims is the very aim of the work. The presenters focus on the following components of the development process: the time to an event, the conditional event type, the conditional payment type if any, and the conditional severity if any. They distinguish three types of events: settlement without any payments made during development, settlement with a payment and a payment without settlement. Given that a payment is to be made, the conditional payment type model describes the probability that it will be one of three claim types, or any possible combination of them. The conditional severity component describes the claim amount structure according to the combination of types paid. The presenters provide appropriate statistical models for each component and calibrate them to the available data. These building blocks are consequently used to predict the future development of open RBNS claims.

Description: Presented at October 1996 Annual Meeting. This session presents a forecasting model of economic assumptions that are inputs to projections of the Social Security system. Social Security projections are made to help policymakers understand the financial stability of the system.

Description: This is the first article in the new "Fireside Chat" series which intends to honor highly accomplised academic actuaries and allow them to share their insights into academic careers with young scholars. This article is mainly based on an interview with Dr. Edward Frees (Jed), one of the most accomplished actuarial professors.

Description: This predictive modeling paper, “Household Life Insurance Demand – A Multivariate Two-Part Model is one of a collection of essays that examine how existing or new methods or models can be applied to the life insurance industry.

Description: In this paper we use expectations of different random variables to define the notions of retrospective, prospective, individual, and aggregate net premiums for plans with contingent obligations.

Description: This paper introduces actuaries to the concept of 'copulas,' a tool for understanding relationships among multivariate outcomes. A copula is a function that links univariate marginals to their full multivariate distribution. Recently, there has been a rapidly developing literature on the statistical properties and applications of copulas. This paper explores some of these practical applications, including estimation of joint life mortality and multidecrement models. In addition, the paper describes basic properties of copulas, their relationships to measures of dependence and several families of copulas that have appeared in the literature. This paper will also be useful to those who wish to use copulas for statistical inference. Statistical inference procedures are illustrated using insurance company data on losses and expenses. For this data, the paper shows how to fit copulas and describes their usefulness by pricing a reinsurance contract and estimating expenses for pre-specified losses.

Description: Full version of Pension Section News, March 2000, Issue No. 42.

Description: This article describes the 2009 actuarial research conference.t

Description: Actuaries and other managers of risk identify factors in modeling insurance risks because [i] they feel that these factors may cause the outcome of a risk, or [ii] that the factors can be managed, thus allowing analysts a degree of control over the system risk. The purpose of this paper is to propose a framework for quantifying the relative importance of a source of risk. The intent is that, with a quantitative measure of relative importance, risk managers will be able to sharpen their intuition about the relative importance of risks and be better custodians of financial security systems. This paper proposes a measure of relative importance that has its roots in both the statistics and economics literatures. The measure is intuitively appealing when assessing the effectiveness of basic risk management techniques including risk exchange, pooling and financial risk management. The risk measure is also shown to be useful in multivariate situations where several factors affect a risk simultaneously. The paper illustrates this usefulness by considering a pool of policies that is subject to mortality, the risk of a disaster that is common to all policies, and to a common investment environment.